Large resource description framework (RDF) knowledge bases containing billions of assertions have been published. The RDF stores also contain large web ontology language (OWL) schemas and/or ontologies with a multitude of concepts and relations that help interpret instance data. However, the OWL ontologies can typically be limited to a sub-class and sub-property hierarchy, and thus do not provide much inferred information by way of reasoning.
Existing association rule mining (ARM) approaches learn rules from large relational data, but such approaches have never been applied to the RDF/OWL case. As such, while relational schemas typically contain tens or hundreds of attributes and/or columns, RDF/OWL schema size is several orders of magnitudes larger, and thus existing ARM approaches cannot scale. Furthermore, many existing ARM approaches operate on simple data structures (wherein, for example, attribute values are lexical values), and are therefore not well suited for the richer structure of RDF/OWL data, where attribute values can themselves be complex objects, which results in large and complex graphs.